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Enhancing Genetic Algorithms by a Trie-Based Complete Solution Archive

  • Günther R. Raidl
  • Bin Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6022)

Abstract

Genetic algorithms (GAs) share a common weakness with most other metaheuristics: Candidate solutions are in general revisited multiple times, lowering diversity and wasting precious CPU time. We propose a complete solution archive based on a special binary trie structure for GAs with binary representations that efficiently stores all evaluated solutions during the heuristic search. Solutions that would later be revisited are detected and effectively transformed into similar yet unconsidered candidate solutions. The archive’s relevant insert, find, and transform operations all run in time O(l) where l is the length of the solution representation. From a theoretical point of view, the archive turns the GA into a complete algorithm with a clear termination condition and bounded run time. Computational results are presented for Royal Road functions and NK landscapes, indicating the practical advantages.

Keywords

genetic algorithms solution archive revisits tries 

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References

  1. 1.
    Aho, A.V., Hopcroft, J.E., Ullman, J.D.: Data Structures and Algorithms. Addison-Wesley, Reading (1985)Google Scholar
  2. 2.
    Battiti, R., Tecchiolli, G.: The reactive tabu search. ORSA Journal on Computing 6, 126–140 (1994)zbMATHGoogle Scholar
  3. 3.
    Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2008)Google Scholar
  4. 4.
    Gantovnik, V.B., Anderson-Cook, C.M., Grdal, Z., Watson, L.T.: A genetic algorithm with memory for mixed discrete-continuous design optimization. Computers and Structures 81, 2003–2009 (2003)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Glover, F.: Tabu search - part II. ORSA Journal on Computing 2(1), 4–32 (1990)zbMATHGoogle Scholar
  6. 6.
    Gusfield, D.: Algorithms on Strings, Trees, and Sequences. Cambridge University Press, Cambridge (1997)zbMATHGoogle Scholar
  7. 7.
    Holland, J.: Adaptation In Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  8. 8.
    Kauffman, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, Oxford (1993)Google Scholar
  9. 9.
    Knuth, D.E.: The Art of Computer Programming Vol. III: Sorting and Searching. Addison-Wesley, Reading (1973)Google Scholar
  10. 10.
    Kratica, J.: Improving performances of the genetic algorithm by caching. Computers and Artificial Intelligence 18(3), 271–283 (1999)zbMATHGoogle Scholar
  11. 11.
    Louis, S.J., Li, G.: Combining robot control strategies using genetic algorithms with memory. In: Angeline, P.J., McDonnell, J.R., Reynolds, R.G., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213, pp. 431–442. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  12. 12.
    Mauldin, M.L.: Maintaining diversity in genetic search. In: AAAI, pp. 247–250 (1984)Google Scholar
  13. 13.
    Mitchell, M., Forrest, S., Holland, J.H.: The royal road for genetic algorithms: Fitness landscapes and GA performance. In: Varela, F.J., Bourgine, P. (eds.) Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, pp. 245–254. MIT Press, Cambridge (1992)Google Scholar
  14. 14.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1992)Google Scholar
  15. 15.
    Raidl, G.R., Gottlieb, J.: On the importance of phenotypic duplicate elimination in decoder-based evolutionary algorithms. In: Brave, S., Wu, A.S. (eds.) Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference, Orlando, FL, pp. 204–211 (1999)Google Scholar
  16. 16.
    Ronald, S.: Complex systems: Mechanism of adaption. In: Stonier, R., Yu, X.H. (eds.) Complex Systems: Mechanism of Adaptation, pp. 133–140. IOS Press, Amsterdam (1994)Google Scholar
  17. 17.
    Ronald, S.: Duplicate genotypes in a genetic algorithm. In: Fogel, D.B., Schwefel, H.P., Bck, T., Yao, X. (eds.) IEEE World Congress on Computational Intelligence (WCCI 1998), pp. 793–798 (1998)Google Scholar
  18. 18.
    Sramko, A.: Enhancing a genetic algorithm by a complete solution archive based on a trie data structure. Master’s thesis, Vienna University of Technology, Institute of Computer Graphic s and Algorithms, Vienna, Austria (February 2009)Google Scholar
  19. 19.
    Yuen, S.Y., Chow, C.K.: A non-revisiting genetic algorithm. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 4583–4590. IEEE Press, Los Alamitos (2007)CrossRefGoogle Scholar
  20. 20.
    Zaubzer, S.: A complete archive genetic algorithm for the multidimensional knapsack problem. Master’s thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria (May 2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Günther R. Raidl
    • 1
  • Bin Hu
    • 1
  1. 1.Institute of Computer Graphics and AlgorithmsVienna University of TechnologyViennaAustria

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